/*
 * Open Source Physics software is free software as described near the bottom of this code file.
 *
 * For additional information and documentation on Open Source Physics please see:
 * <http://www.opensourcephysics.org/>
 */

/*
 * The org.opensourcephysics.media.gif package provides animated gif
 * implementations of the Video and VideoRecorder interfaces.
 *
 * Copyright (c) 2004  Douglas Brown and Wolfgang Christian.
 *
 * This is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 *
 * This software is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston MA 02111-1307 USA
 * or view the license online at http://www.gnu.org/copyleft/gpl.html
 *
 * For additional information and documentation on Open Source Physics,
 * please see <http://www.opensourcephysics.org/>.
 */
package org.opensourcephysics.media.gif;
/* NeuQuant Neural-Net Quantization Algorithm
 * ------------------------------------------
 *
 * Copyright (c) 1994 Anthony Dekker
 *
 * NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994.
 * See "Kohonen neural networks for optimal colour quantization"
 * in "Network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367.
 * for a discussion of the algorithm.
 *
 * Any party obtaining a copy of these files from the author, directly or
 * indirectly, is granted, free of charge, a full and unrestricted irrevocable,
 * world-wide, paid up, royalty-free, nonexclusive right and license to deal
 * in this software and documentation files (the "Software"), including without
 * limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
 * and/or sell copies of the Software, and to permit persons who receive
 * copies from any such party to do so, with the only requirement being
 * that this copyright notice remain intact.
 */
// Ported to Java 12/00 K Weiner

/** A class to provide color quantization for GIF */
public class NeuQuant {
  protected static final int netsize = 256;               /* number of colours used */
  /* four primes near 500 - assume no image has a length so large */
  /* that it is divisible by all four primes */
  protected static final int prime1 = 499;
  protected static final int prime2 = 491;
  protected static final int prime3 = 487;
  protected static final int prime4 = 503;
  protected static final int minpicturebytes = (3*prime4);
  /* minimum size for input image */

  /* Program Skeleton
     ----------------
     [select samplefac in range 1..30]
     [read image from input file]
     pic = (unsigned char*) malloc(3*width*height);
     initnet(pic,3*width*height,samplefac);
     learn();
     unbiasnet();
     [write output image header, using writecolourmap(f)]
     inxbuild();
     write output image using inxsearch(b,g,r)      */
  /* Network Definitions
     ------------------- */
  protected static final int maxnetpos = (netsize-1);
  protected static final int netbiasshift = 4;            /* bias for colour values */
  protected static final int ncycles = 100;               /* no. of learning cycles */
  /* defs for freq and bias */
  protected static final int intbiasshift = 16;           /* bias for fractions */
  protected static final int intbias = (1<<intbiasshift);
  protected static final int gammashift = 10;             /* gamma = 1024 */
  protected static final int gamma = (1<<gammashift);
  protected static final int betashift = 10;
  protected static final int beta = (intbias>>betashift); /* beta = 1/1024 */
  protected static final int betagamma = (intbias<<(gammashift-betashift));
  /* defs for decreasing radius factor */
  protected static final int initrad = (netsize>>3);
  /* for 256 cols, radius starts */
  protected static final int radiusbiasshift = 6;
  /* at 32.0 biased by 6 bits */
  protected static final int radiusbias = (1<<radiusbiasshift);
  protected static final int initradius = (initrad*radiusbias);
  /* and decreases by a */
  protected static final int radiusdec = 30;              /* factor of 1/30 each cycle */
  /* defs for decreasing alpha factor */
  protected static final int alphabiasshift = 10;         /* alpha starts at 1.0 */
  protected static final int initalpha = (1<<alphabiasshift);
  protected int alphadec;                                 /* biased by 10 bits */
  /* radbias and alpharadbias used for radpower calculation */
  protected static final int radbiasshift = 8;
  protected static final int radbias = (1<<radbiasshift);
  protected static final int alpharadbshift = (alphabiasshift+radbiasshift);
  protected static final int alpharadbias = (1<<alpharadbshift);
  /* Types and Global Variables
    -------------------------- */
  protected byte[] thepicture;                            /* the input image itself */
  protected int lengthcount;                              /* lengthcount = H*W*3 */
  protected int samplefac;                                /* sampling factor 1..30 */
  //   typedef int pixel[4];                /* BGRc */
  protected int[][] network;                              /* the network itself - [netsize][4] */
  protected int[] netindex = new int[256];
  /* for network lookup - really 256 */

  protected int[] bias = new int[netsize];
  /* bias and freq arrays for learning */
  protected int[] freq = new int[netsize];
  protected int[] radpower = new int[initrad];
  /* radpower for precomputation */

  /* Initialise network in range (0,0,0) to (255,255,255) and set parameters
     ----------------------------------------------------------------------- */
  NeuQuant(byte[] thepic, int len, int sample) {
    int i;
    int[] p;
    thepicture = thepic;
    lengthcount = len;
    samplefac = sample;
    network = new int[netsize][];
    for(i = 0; i<netsize; i++) {
      network[i] = new int[4];
      p = network[i];
      p[0] = p[1] = p[2] = (i<<(netbiasshift+8))/netsize;
      freq[i] = intbias/netsize; /* 1/netsize */
      bias[i] = 0;
    }
  }

  byte[] colorMap() {
    byte[] map = new byte[3*netsize];
    int[] index = new int[netsize];
    for(int i = 0; i<netsize; i++) {
      index[network[i][3]] = i;
    }
    int k = 0;
    for(int i = 0; i<netsize; i++) {
      int j = index[i];
      map[k++] = (byte) (network[j][0]);
      map[k++] = (byte) (network[j][1]);
      map[k++] = (byte) (network[j][2]);
    }
    return map;
  }

  /* Insertion sort of network and building of netindex[0..255] (to do after unbias)
     ------------------------------------------------------------------------------- */
  void inxbuild() {
    int i, j, smallpos, smallval;
    int[] p;
    int[] q;
    int previouscol, startpos;
    previouscol = 0;
    startpos = 0;
    for(i = 0; i<netsize; i++) {
      p = network[i];
      smallpos = i;
      smallval = p[1];      /* index on g */
      /* find smallest in i..netsize-1 */
      for(j = i+1; j<netsize; j++) {
        q = network[j];
        if(q[1]<smallval) { /* index on g */
          smallpos = j;
          smallval = q[1];  /* index on g */
        }
      }
      q = network[smallpos];
      /* swap p (i) and q (smallpos) entries */
      if(i!=smallpos) {
        j = q[0];
        q[0] = p[0];
        p[0] = j;
        j = q[1];
        q[1] = p[1];
        p[1] = j;
        j = q[2];
        q[2] = p[2];
        p[2] = j;
        j = q[3];
        q[3] = p[3];
        p[3] = j;
      }
      /* smallval entry is now in position i */
      if(smallval!=previouscol) {
        netindex[previouscol] = (startpos+i)>>1;
        for(j = previouscol+1; j<smallval; j++) {
          netindex[j] = i;
        }
        previouscol = smallval;
        startpos = i;
      }
    }
    netindex[previouscol] = (startpos+maxnetpos)>>1;
    for(j = previouscol+1; j<256; j++) {
      netindex[j] = maxnetpos; /* really 256 */
    }
  }

  /* Main Learning Loop
     ------------------ */
  void learn() {
    int i, j, b, g, r;
    int radius, rad, alpha, step, delta, samplepixels;
    byte[] p;
    int pix, lim;
    if(lengthcount<minpicturebytes) {
      samplefac = 1;
    }
    alphadec = 30+((samplefac-1)/3);
    p = thepicture;
    pix = 0;
    lim = lengthcount;
    samplepixels = lengthcount/(3*samplefac);
    delta = samplepixels/ncycles;
    alpha = initalpha;
    radius = initradius;
    rad = radius>>radiusbiasshift;
    if(rad<=1) {
      rad = 0;
    }
    for(i = 0; i<rad; i++) {
      radpower[i] = alpha*(((rad*rad-i*i)*radbias)/(rad*rad));
    }
    //fprintf(stderr,"beginning 1D learning: initial radius=%d\n", rad);
    if(lengthcount<minpicturebytes) {
      step = 3;
    } else if((lengthcount%prime1)!=0) {
      step = 3*prime1;
    } else {
      if((lengthcount%prime2)!=0) {
        step = 3*prime2;
      } else {
        if((lengthcount%prime3)!=0) {
          step = 3*prime3;
        } else {
          step = 3*prime4;
        }
      }
    }
    i = 0;
    while(i<samplepixels) {
      b = (p[pix+0]&0xff)<<netbiasshift;
      g = (p[pix+1]&0xff)<<netbiasshift;
      r = (p[pix+2]&0xff)<<netbiasshift;
      j = contest(b, g, r);
      altersingle(alpha, j, b, g, r);
      if(rad!=0) {
        alterneigh(rad, j, b, g, r); /* alter neighbours */
      }
      pix += step;
      if(pix>=lim) {
        pix -= lengthcount;
      }
      i++;
      if(delta==0) {
        delta = 1;
      }
      if(i%delta==0) {
        alpha -= alpha/alphadec;
        radius -= radius/radiusdec;
        rad = radius>>radiusbiasshift;
        if(rad<=1) {
          rad = 0;
        }
        for(j = 0; j<rad; j++) {
          radpower[j] = alpha*(((rad*rad-j*j)*radbias)/(rad*rad));
        }
      }
    }
    //fprintf(stderr,"finished 1D learning: final alpha=%f !\n",((float)alpha)/initalpha);
  }

  /* Search for BGR values 0..255 (after net is unbiased) and return colour index
   ---------------------------------------------------------------------------- */
  int map(int b, int g, int r) {
    int i, j, dist, a, bestd;
    int[] p;
    int best;
    bestd = 1000;    /* biggest possible dist is 256*3 */
    best = -1;
    i = netindex[g]; /* index on g */
    j = i-1;         /* start at netindex[g] and work outwards */
    while((i<netsize)||(j>=0)) {
      if(i<netsize) {
        p = network[i];
        dist = p[1]-g; /* inx key */
        if(dist>=bestd) {
          i = netsize; /* stop iter */
        } else {
          i++;
          if(dist<0) {
            dist = -dist;
          }
          a = p[0]-b;
          if(a<0) {
            a = -a;
          }
          dist += a;
          if(dist<bestd) {
            a = p[2]-r;
            if(a<0) {
              a = -a;
            }
            dist += a;
            if(dist<bestd) {
              bestd = dist;
              best = p[3];
            }
          }
        }
      }
      if(j>=0) {
        p = network[j];
        dist = g-p[1]; /* inx key - reverse dif */
        if(dist>=bestd) {
          j = -1;      /* stop iter */
        } else {
          j--;
          if(dist<0) {
            dist = -dist;
          }
          a = p[0]-b;
          if(a<0) {
            a = -a;
          }
          dist += a;
          if(dist<bestd) {
            a = p[2]-r;
            if(a<0) {
              a = -a;
            }
            dist += a;
            if(dist<bestd) {
              bestd = dist;
              best = p[3];
            }
          }
        }
      }
    }
    return(best);
  }

  byte[] process() {
    learn();
    unbiasnet();
    inxbuild();
    return colorMap();
  }

  /* Unbias network to give byte values 0..255 and record position i to prepare for sort
     ----------------------------------------------------------------------------------- */
  void unbiasnet() {
    int i;
    for(i = 0; i<netsize; i++) {
      network[i][0] >>= netbiasshift;
      network[i][1] >>= netbiasshift;
      network[i][2] >>= netbiasshift;
      network[i][3] = i; /* record colour no */
    }
  }

  /* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
     --------------------------------------------------------------------------------- */
  protected void alterneigh(int rad, int i, int b, int g, int r) {
    int j, k, lo, hi, a, m;
    int[] p;
    lo = i-rad;
    if(lo<-1) {
      lo = -1;
    }
    hi = i+rad;
    if(hi>netsize) {
      hi = netsize;
    }
    j = i+1;
    k = i-1;
    m = 1;
    while((j<hi)||(k>lo)) {
      a = radpower[m++];
      if(j<hi) {
        p = network[j++];
        try {
          p[0] -= (a*(p[0]-b))/alpharadbias;
          p[1] -= (a*(p[1]-g))/alpharadbias;
          p[2] -= (a*(p[2]-r))/alpharadbias;
        } catch(Exception e) {

        /** empty block */
        } // prevents 1.3 miscompilation
      }
      if(k>lo) {
        p = network[k--];
        try {
          p[0] -= (a*(p[0]-b))/alpharadbias;
          p[1] -= (a*(p[1]-g))/alpharadbias;
          p[2] -= (a*(p[2]-r))/alpharadbias;
        } catch(Exception e) {

        /** empty block */
        }
      }
    }
  }

  /* Move neuron i towards biased (b,g,r) by factor alpha
     ---------------------------------------------------- */
  protected void altersingle(int alpha, int i, int b, int g, int r) {
    /* alter hit neuron */
    int[] n = network[i];
    n[0] -= (alpha*(n[0]-b))/initalpha;
    n[1] -= (alpha*(n[1]-g))/initalpha;
    n[2] -= (alpha*(n[2]-r))/initalpha;
  }

  /* Search for biased BGR values
     ---------------------------- */
  protected int contest(int b, int g, int r) {
    /* finds closest neuron (min dist) and updates freq */
    /* finds best neuron (min dist-bias) and returns position */
    /* for frequently chosen neurons, freq[i] is high and bias[i] is negative */
    /* bias[i] = gamma*((1/netsize)-freq[i]) */
    int i, dist, a, biasdist, betafreq;
    int bestpos, bestbiaspos, bestd, bestbiasd;
    int[] n;
    bestd = ~(1<<31);
    bestbiasd = bestd;
    bestpos = -1;
    bestbiaspos = bestpos;
    for(i = 0; i<netsize; i++) {
      n = network[i];
      dist = n[0]-b;
      if(dist<0) {
        dist = -dist;
      }
      a = n[1]-g;
      if(a<0) {
        a = -a;
      }
      dist += a;
      a = n[2]-r;
      if(a<0) {
        a = -a;
      }
      dist += a;
      if(dist<bestd) {
        bestd = dist;
        bestpos = i;
      }
      biasdist = dist-((bias[i])>>(intbiasshift-netbiasshift));
      if(biasdist<bestbiasd) {
        bestbiasd = biasdist;
        bestbiaspos = i;
      }
      betafreq = (freq[i]>>betashift);
      freq[i] -= betafreq;
      bias[i] += (betafreq<<gammashift);
    }
    freq[bestpos] += beta;
    bias[bestpos] -= betagamma;
    return(bestbiaspos);
  }

}

/*
 * Open Source Physics software is free software; you can redistribute
 * it and/or modify it under the terms of the GNU General Public License (GPL) as
 * published by the Free Software Foundation; either version 2 of the License,
 * or(at your option) any later version.

 * Code that uses any portion of the code in the org.opensourcephysics package
 * or any subpackage (subdirectory) of this package must must also be be released
 * under the GNU GPL license.
 *
 * This software is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston MA 02111-1307 USA
 * or view the license online at http://www.gnu.org/copyleft/gpl.html
 *
 * Copyright (c) 2007  The Open Source Physics project
 *                     http://www.opensourcephysics.org
 */
